How Can I Ensure Sequential Execution of Functions in a Concurrent Programming Environment?

When working with concurrent programming in Python, managing the execution order of functions can be particularly challenging due to the nature of asynchronous and independent processes. I recently faced a similar challenge where I needed to ensure that two functions (mp and generate_info) executed concurrently, but a third function (create_final) should only start after the first two had finished. This might seem straightforward, but when dealing with concurrency, simple tasks can easily become complex.

Understanding the Problem:

Before jumping into solving the issue, let’s break down the problem using the provided code and identify where things are going awry. Here’s the segment of code that’s causing the trouble:

main_process = multiprocessing.Process(target=mp, args=[arguments, cb_fn])
side_process = RepeatTimer(30, generate_info)
final_process = multiprocessing.Process(target=create_final)

main_process.start()
side_process.start()
main_process.join()
side_process.cancel()
final_process.start()
final_process.join()

From this code, main_process and side_process are intended to run concurrently. main_process is straightforward—it’s a Process object from Python’s multiprocessing module, running the mp function. side_process, however, uses a custom class RepeatTimer that extends threading.Timer, designed to execute generate_info repeatedly every 30 seconds until it’s canceled.

The Core Issue:

The join() method on main_process effectively blocks until mp finishes execution. However, side_process, which uses the threading module, isn’t joined in the same way—side_process.cancel() merely signals the timer to stop after main_process finishes, but does not wait for a current invocation of generate_info to complete. Consequently, final_process may start while generate_info is still running.

Optimal Solution:

To enforce the desired execution order—running create_final strictly after mp and generate_info are both completely done—we need to ensure side_process is joined properly. Here’s a revised approach:

1. Ensure Thread Synchronization: Implement a way to join the RepeatTimer. Since threading.Timer doesn’t provide a built-in join method like multiprocessing.Process, we need to add synchronization manually to wait for its completion.

1. Modify the RepeatTimer Class: Introduce a mechanism to ensure that the timer can be joined safely after canceling.

Here’s a possible modification for the RepeatTimer class:

import threading

class RepeatTimer(threading.Timer):
    def __init__(self, interval, function, args=None, kwargs=None):
        super().__init__(interval, function, args, kwargs)
        self.finished = threading.Event()

    def run(self):
        while not self.finished.is_set():
            self.finished.wait(self.interval)
            if not self.finished.is_set():
                self.function(*self.args, **self.kwargs)

    def cancel(self):
        self.finished.set()

    def join(self):
        self.finished.wait()

And the way you would use it in your parallel_process function:

def parallel_process(mp, arguments, cb_fn):
    main_process = multiprocessing.Process(target=mp, args=[arguments, cb_fn])
    side_process = RepeatTimer(30, generate_info)
    final_process = multiprocessing.Process(target=create_final)

    main_process.start()
    side_process.start()
    main_process.join()
    side_process.cancel()
    side_process.join()
    final_process.start()
    final_process.join()

With these changes, the RepeatTimer now waits for the full interval if it’s currently executing generate_info, even if you call cancel(). This guarantees that side_process.join() will hold up execution until it’s fully done. This correct synchronization around side_process ensures that create_final will only start running after both mp and generate_info have completely finished.

Addressing Concurrency More Broadly:

Always remember, while working with concurrent programming, orchestrating the execution order and handling synchronization between threads/processes are crucial. Using synchronization primitives like Event or Semaphore, or converting your architecture to use concurrent.futures with more controlled execution flows like Futures and Executor can simplify management of concurrent tasks and their respective dependencies.